整合基于病原体的疾病、流动性和废水模型,以研究传染病的传播。

IF 1 4区 医学 Q4 HEALTH CARE SCIENCES & SERVICES
Néstor DelaPaz-Ruíz, Ellen-Wien Augustijn, Mahdi Farnaghi, Sheheen A Abdulkareem, Raul Zurita Milla
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引用次数: 0

摘要

在2019冠状病毒病暴发期间,利用基于废水的流行病学监测导致该疾病的病毒SARS-CoV-2的传播。然而,由于需要额外的方法来准确地将废水中的病毒浓度转化为疾病阳性的人类计数,这种方法受到限制。对COVID-19病例及其在废水中的致病病毒SARS-CoV-2的浓度进行综合建模,必然会加深我们的理解。然而,这需要解决疾病、人口流动和废水模型之间的技术差异。为此,我们开发了一个综合的基于agent的模型(ABM),便于在空间和时间上以各种时间分辨率进行分析,包括疾病传播、人口流动和废水产生,同时对不同类型的传染病或病原体也具有足够的通用性。该综合模型复制了COVID-19的流行曲线,可以估计家庭层面的每日感染人数,从而可以监测感染强度的空间格局。此外,该模型还可以监测整个污水处理厂的受感染废水随时间和空间的估计产量。该模型解决了不同决议之间的差异,并可能支持未来流行病的早期预警系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrating agent-based disease, mobility and wastewater models for the study of the spread of communicable diseases.

Wastewater-based epidemiology was utilized during the COVID-19 outbreak to monitor the circulation of SARS-CoV-2, the virus causing this disease. However, this approach is limited by the need for additional methods to accurately translate virus concentrations in wastewater to disease-positive human counts. Combined modelling of COVID-19 disease cases and the concentration of its causative virus, SARS-CoV-2, in wastewater will necessarily deepen our understanding. However, this requires addressing the technical differences between disease, population mobility and wastewater models. To that end, we developed an integrated Agent-Based Model (ABM) that facilitates analysis in space and time at various temporal resolutions, including disease spread, population mobility and wastewater production, while also being sufficiently generic for different types of infectious diseases or pathogens. The integrated model replicates the epidemic curve for COVID-19 and can estimate the daily infections at the household level, enabling the monitoring of the spatial patterns of infection intensity. Additionally, the model allows monitoring the estimated production of infected wastewater over time and spatially across the sewage and treatment plant. The model addresses differences between resolutions and can potentially support Early Warning Systems (EWS) for future pandemics.

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来源期刊
Geospatial Health
Geospatial Health 医学-公共卫生、环境卫生与职业卫生
CiteScore
2.40
自引率
11.80%
发文量
48
审稿时长
12 months
期刊介绍: The focus of the journal is on all aspects of the application of geographical information systems, remote sensing, global positioning systems, spatial statistics and other geospatial tools in human and veterinary health. The journal publishes two issues per year.
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